This invention relates to an apparatus for estimating the traffic condition value of elevators in which the traffic condition value such as the numbers of persons ascending and descending with the elevators or the service states of the elevators is estimated on the basis of a measured value.
A traffic condition value in an elevator system, for example, the number of persons who ascend and descend the elevators, fluctuates irregularly when closely observed within a period of one day, but presents similar aspects for the same time zones when observed over several days. In, for example, an office building, elevator passengers on their way to their office floors crowd on the first floor during a short period of time in the time zone in which they attend offices in the morning. In the first half of the lunch hour, many passengers go from the office floors to a restaurant floor, while in the latter half thereof, many passengers go from the restaurant floor and the first floor to the office floors. Further, many passengers go from the office floors to the first floor in the time zone in which they leave the offices in the evening. The volumes of traffic in the up direction and in the down direction are nearly equal to the daytime time zones other than mentioned above, while the volume of traffic becomes very small throughout the nighttime.
In order to deal with the traffic in a building changing in a manner described above and having a limited number of elevators, the elevators are usually operated under group supervision. One of the important roles of the group supervision of the elevators is to assign an appropriate elevator to each hall call registered. Various assignment systems for the hall calls have been proposed.
By way of example, there has been adopted a system wherein, when a hall call is registered anew, it is tentatively assigned to respective elevators, and the waiting times of all hall calls, the possibility of full capacity of passengers, etc. are estimated so as to select the appropriate elevator from among the elevators. In order to execute such estimative calculations, data on a traffic condition value peculiar to each building is required. For example, data on the number of passengers who get on and off the cage of each elevator at intermediate floors is required for estimating the possibility of full capacity as the traffic condition varies. When such traffic demand value, which changes every moment, is stored each time, an enormous memory capacity is necessitated, which is not practical. It is therefore common practice to reduce the required memory size by dividing the operating period of time in one day into several time zones and storing only the average traffic demand values of the respective time zones. Soon after the completion of the building, however, there is a high possibility that the traffic demand value will change in accordance with changes in personnel organization in the building. In order to precisely estimate the traffic demand value even against such changes of the personnel organization, there has been proposed a system wherein the traffic demand value in the building is measured, and the estimated traffic demand is sequentially corrected to follow the change of the traffic condition value.
More specifically, the operating period of time in one day is divided into K time zones (hereinbelow, termed "sections"), and a time (hereinbelow, termed "boundary") by which a section k-1 and a section k are bounded is denoted by t.sub.k (k=2, 3, . . . , K). Times t.sub.1 and t.sub.k+1 are the starting time and end time of the elevator operation, respectively. The average traffic condition value P.sub.k (l) of the section k on the l-th day is supposed to be given by the following Equation (1): ##EQU1##
Here, X.sub.k.sup.u (l) is a column vector of (F-1) dimensions (where F denotes the number of floors) the elements of which are the number of passengers to get on cages in the up direction at respective floors in the time zone k of the l-th day. Similarly, X.sub.k.sup.d (l), Y.sub.k.sup.u (l) and Y.sub.k.sup.d (l) are column vectors which indicate the number of passengers to get on the cages in the down direction, the number of passengers to get off the cages in the up direction and the number of passengers to get off the cages in the down direction, respectively. The average traffic condition value P.sub.k (l) is measured by a passenger-number detector which utilizes load changes during the stoppage of the cages of the elevators and/or industrial television, ultrasonic wave, or the like.
First, the case where the representative value of the average traffic condition value P.sub.k (l) of each time zone having a fixed boundary time t.sub.k is sequentially corrected is considered.
It is thought that the columns {P.sub.k (1), P.sub.k (2), . . . } of the average traffic demand values measured daily will disperse in the vicinity of a certain representative value P.sub.k. Since the magnitude of the representative value P.sub.k is unknown, it needs to be estimated. In this case, there is the possibility that the magnitude itself of the representative value P.sub.k will change. The representative value is therefore estimated by taking a linear weighted average given in Equations (2) and (3) below, whereby more importance is attached to the average traffic demand value P.sub.k (l) measured latest than to the other average traffic condition values P.sub.k (1), P.sub.k (2), . . . and P.sub.k (l-1). ##EQU2##
Here, P.sub.k (l) is the representative value which has been estimated for the average traffic demand values P.sub.k (l), . . . , and P.sub.k (l) measured till the l-th day, and P.sub.k (O) is an initial value which is set at a suitable value in advance. .lambda..sub.i denotes the weight of the average traffic demand value P.sub.k (i) measured on the i-th day, and this weight changes depending upon a parameter a as expressed by Equation (3). More specifically, an increase in the value of the parameter a results in an estimation in which more importance is attached to the latest measured average traffic demand value P.sub.k (l) than to the other average traffic demand values P.sub.k (1), . . . and P.sub.k (l-1), and in which the estimated representative value P.sub.k (l) quickly follows up the change of the representative value P.sub.k. However, when the value of the parameter a is too large, it is feared that the estimated representative value will change too violently in a manner to be influenced by the random variation of daily data. Meanwhile, Equations (2) and (3) can be rewritten as follows: EQU P.sub.k (l)=(1-a)P.sub.k (l-1)+aP.sub.k (l) (4) EQU P.sub.k (O)=P.sub.k (O) (5)
In accordance with the above Equations (4) and (5), there is the advantage that the weighted average of Equation (2) can be calculated without storing the measurement values P.sub.k (i)(i=1, 2, . . . , l-1) of the average traffic demand values in the past.
However, even when a traffic demand value which fluctuates cyclically on weekdays becomes an extremely different magnitude on Sunday, a national holiday or the like or when a irregular traffic demand value whose magnitude abruptly increases temporarily arises as immediately before the starting or after the end of a conference or an assembly, the measured result of such magnitude has heretofore been adopted for the estimation of the traffic demand value without being distinguished from the others. This has sometimes led to the drawback that the estimated value causes a great difference from the actual traffic demand value on the weekday, so the elevators are not group-supervised as intended.